Bubble Chart in Plotly

#library(ggplot2)
library(plotly)
library(dplyr)
library(Hmisc) # %nin% 

data <- read.csv("owid-covid-data.csv")

data <- data %>% 
  group_by(location) %>%
  summarise(continent = max(continent),
    pop = max(population,na.rm = TRUE),
    cases = max(total_cases,na.rm = TRUE),
    deaths = max(total_deaths,na.rm = TRUE),
    vac1 = max(people_vaccinated - people_fully_vaccinated,
               na.rm = TRUE), 
    vac2 = max(people_fully_vaccinated,na.rm = TRUE))

data <- data %>% mutate(cases = ifelse(!(cases>=0), 0, cases),
                        deaths = ifelse(!(deaths>=0), 0, deaths),
                        vac1 = ifelse(!(vac1>=0 ), 0, vac1), 
                        vac2 = ifelse(!(vac2>=0), 0, vac2))

glimpse(data)

## Rows: 229
## Columns: 7
## $ location  <chr> "Afghanistan", "Africa", "Albania", "Algeria", "Andorra", "A~
## $ continent <chr> "Asia", "", "Europe", "Africa", "Europe", "Africa", "North A~
## $ pop       <dbl> 38928341, 1340598113, 2877800, 43851043, 77265, 32866268, 15~
## $ cases     <dbl> 91458, 5057604, 132461, 133742, 13826, 36790, 0, 1263, 41454~
## $ deaths    <dbl> 3612, 135003, 2453, 3579, 127, 836, 0, 42, 86029, 4488, 0, 7~
## $ vac1      <dbl> 393254, 19098256, 259335, 0, 22909, 546182, 5332, 29279, 970~
## $ vac2      <dbl> 177266, 11546311, 332173, 0, 10938, 447704, 5818, 23844, 333~

data <- data %>% filter(location %nin% c("World",
                                         "Asia",
                                         "Europe",
                                         "North America",
                                         "European Union",
                                         "South America",
                                         "Africa"))

#Estados Unidos: US$ 20,933 trilhões
#China: US$ 14,723 trilhões
#Japão: US$ 5,049 trilhões
#Alemanha: US$ 3,803 trilhões
#Reino Unido: US$ 2,711 trilhões
#Índia: US$ 2,709 trilhões
#França: US$ 2,599 trilhões
#Itália: US$ 1,885 trilhão
#Canadá: US$ 1,643 trilhão
#Coreia do Sul: US$ 1,631 trilhão
#Rússia: US$ 1,474 trilhão
#Brasil: US$ 1,434 trilhão
#Austrália: US$ 1,359 trilhão
#Espanha: US$ 1,278 trilhão
#México: US$ 1,076 trilhão

names <- c('Brazil',
          'United States',
          'Canada',
          'Mexico',
          'Germany',
          'United Kingdom',
          'French',
          'Italy',
          'Spain',
          'Russia',
          'India',
          'South Korea',
          'China',
          'Japan',
          'Australia')


colors <- c('#F28B30', # Asia (laranja)
            '#BF0A3A', # Europa (vermelho)
            '#022873', # América do norte (azul)
            '#F23D6D', # Oceania (rosa)
            'gray',    # Outros (cinza)
            '#03A62C') # América do sul (verde)

fig <- plot_ly(data, x = ~ (cases/pop) * 100,
               y = ~ (deaths/pop) * 100,
               text = ~location,
               type = 'scatter',
               mode = 'markers',
               marker = list(size = ~ (vac2/pop) * 100,
                             opacity = 0.5, 
                             color = 'blue'))

fig <- fig %>% layout(title = "COVID-19 vaccinations of top 15 GPD countries",
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig